A Tutorial on Learned Multidimensional Indexes*~
Abdullah Al Mamun (Ph.D. Student)
Hao Wu (Undergraduate Student)
Walid G. Aref
Department of Computer Science
West Lafayette, Indiana 47907, U.S.A.
*We acknowledge the support of the National Science Foundation under Grant Numbers III-1815796 and IIS-1910216.
~This page is based on a tutorial given at the 2020 ACM SIGSPATIAL Conference:
Abdullah Al-Mamun, Hao Wu, Walid G. Aref: A Tutorial on Learned Multi-dimensional Indexes. ACM SIGSPATIAL Conference, pp. 1-4, Nov. 2020. (pdf of the tutorial proposal).
Recently, Machine Learning (ML, for short) has been successfully applied to database indexing. Initial experimentation on Learned Indexes has demonstrated better search performance and lower space requirements than their traditional database counter parts. Numerous attempts have been explored to extend learned indexes to the multi-dimensional space. This makes learned indexes potentially suitable for spatial databases. The goal of this tutorial is to provide up-to-date coverage of learned indexes both in the single and multidimensional spaces. The tutorial covers over 25 learned indexes. The tutorial navigates through the space of learned indexes through a taxonomy that helps classify the covered learned indexes both in the single and multi-dimensional spaces.
OUTLINE OF THE TAXONOMY
An updated version of the taxonomy is given below.
In the taxonomy, we distinguish between two main approaches:
- Indexing the Learned Models vs.
- Learning the Index
By indexing the learned models, we refer to the following problem. Assume that we are given a collection of learned models, where each model represents (and helps recognize) a certain object class, e.g., a learned model that represents the class dog, another that represents that class cat, etc. Given a query object, one needs to execute each of the models to identify that model that produces the highest matching score for the given query object. The question is:
Can we index the learned models to speed up the matching process?
Several indexes have been proposed that index these learned models to speed up the matching process (e.g., see [1,46]).
In contrast, by learning the index, we refer to the problem of substituting a traditional database index, e.g., a B+-tree, by a machine-learning based model. Instead of searching the B+-tree to locate the leaf page that contains an input search key, one uses an ML model that predicts the location (or the leaf page) that contains the search key .
In the tutorial, we give example indexes that follow each of the two approaches.
In the taxonomy, we further distinguish between learned indexes along the following dimension:
- Learned indexes that support static data sets vs.
- Learned indexes that support updates
Notice that the issue of supporting static vs. dynamic data sets is crucial due to the fact that learning an index needs offline training that is relatively slow in nature. Thus, learned indexes that support updates need to accommodate for this fact and still support online response.
Next, we distinguish between learned indexes along the dimensionality of the data.
- Learned indexes for one-dimensional data vs.
- Learned indexes for multi-dimensional data
Finally, we close by discussing open research problems.
Extended Version of the slides (V2.0): <slides>
In this version, in addition to covering more learned multi-dimensional indexes, we extend the taxonomy to cover the following new dimension:
- Learned indexes with fixed data layout vs.
- Learned indexes with dynamic data layout
Abdullah Al Mamun
Abdullah is a PhD student at the Department of Computer Science (CS), Purdue University. His research interests are in the area of Learned Index Structures, particularly, in the area of Learned Multidimensional and Spatial Indexes. Previously, he completed his MS in CS from Memorial University of Newfoundland, Canada.
Hao is a senior undergraduate student at Purdue University with majors in Data Science, Statistics-Math, Aviation Management. He is interested in ML-oriented research as well as its application in data-driven multi-disciplinary projects.
Walid G. Aref
Walid is a professor of computer science at Purdue. His research interests are in extending the functionality of database systems in support of emerging applications, e.g., spatial, spatio-temporal, graph, biological, and sensor databases. His focus is on query processing, indexing, data streaming, and geographic information systems (GIS). Walid’s research has been supported by the National Science Foundation, the National Institute of Health, Purdue Research Foundation, CERIAS, Panasonic, and Microsoft Corp. In 2001, he received the CAREER Award from the National Science Foundation and in 2004, he received a Purdue University Faculty Scholar award. Walid is a member of Purdue’s CERIAS. He is the Editor-in-Chief of the ACM Transactions of Spatial Algorithms and Systems (ACM TSAS), an editorial board member of the Journal of Spatial Information Science (JOSIS), and has served as an editor of the VLDB Journal and the ACM Transactions of Database Systems (ACM TODS). Walid has won several best paper awards including the 2016 VLDB ten-year best paper award. He is a Fellow of the IEEE, and a member of the ACM. Between 2011 and 2014, Walid has served as the chair of the ACM Special Interest Groupon Spatial Information (SIGSPATIAL).
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